Gaussian Approximation Potentials: a brief tutorial introduction

Albert P. Bartók; Gábor Csányi

Gaussian Approximation Potentials: a brief tutorial introduction

Materials Science 2020-02-06 v2 Chemical Physics

Authors: Albert P. Bartók , Gábor Csányi

Abstract

We present a swift walk-through of our recent work that uses machine learning to fit interatomic potentials based on quantum mechanical data. We describe our Gaussian Approximation Potentials (GAP) framework, discussing a variety of descriptors, how to train the model on total energies and derivatives and the simultaneous use of multiple models. We also show a small example using QUIP, the software sandbox implementation of GAP that is available for non-commercial use.

Keywords

gaussian processes machine learning for materials science statistical algorithms

Cite

@article{arxiv.1502.01366,
  title  = {Gaussian Approximation Potentials: a brief tutorial introduction},
  author = {Albert P. Bartók and Gábor Csányi},
  journal= {arXiv preprint arXiv:1502.01366},
  year   = {2020}
}

Comments

3 figures, 20 pages

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